PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Discriminative frequent subgraph mining with optimality guarantees
Marisa Thoma, Hong Cheng, Arthur Gretton, Jiawei Han, Hans-Peter Kriegel, Alexander J. Smola, Le Song, Philip S. Yu, Xifeng Yan and Karsten Borgwardt
Journal of Statistical Analysis and Data Mining Volume 3, Number 5, pp. 302-318, 2010.

Abstract

The goal of frequent subgraph mining is to detect subgraphs that frequently occur in a dataset of graphs. In classification settings, one is often interested in discovering discriminative frequent subgraphs, whose presence or absence is indicative of the class membership of a graph. In this article, we propose an approach to feature selection on frequent subgraphs, called CORK, that combines two central advantages. First, it optimizes a submodular quality criterion, which means that we can yield a near-optimal solution using greedy feature selection. Second, our submodular quality function criterion can be integrated into gSpan, the state-of-the-art tool for frequent subgraph mining, and help to prune the search space for discriminative frequent subgraphs even during frequent subgraph mining. Copyright © 2010 Wiley Periodicals, Inc. Statistical Analysis and Data Mining 3: 302-318, 2010

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:7995
Deposited By:Karsten Borgwardt
Deposited On:17 March 2011